SaoMNK Tutorial: Generalizing NK Epistatic Interaction Fitness
S. Downing
2025-02-12
This project begins from the simple premise that the task of
endogenizing the NK search landscape can be done by representing the
search environment as a binary bipartite network of M
actors affiliating with N components. This bipartite
network can be then be analyzed according to the Stochastic
Actor-Oriented Model (SAOM) (Snijders,
1996).
Thus, the Stochastic Actor-Oriented MNK model, abbrivated
SaoMNK, is designed for running strategic search
simulation, testing, and experimentation by leveraging the
RSiena package, an R implementation of
Simulation Investigation for Empirical Network Analysis (SIENA).
This tutorial offers a basic introduction to SaoMNK.
############### Load R6 Class DEPENDENCIES ############################
## Biparite Environment Search Simulation Class
SaomNkRSienaBiEnv <- source(file.path(dir_r, 'SAOM_NK_R6_model.R'))$value
# ## RSiena search Class
# SaomNkRSienaBiEnv_search_rsiena <- source(file.path(dir_proj, 'SAOM_NK_R6_search_rsiena_model.R'))$value
## default settings: Users do not change; TODO: implment within restricted class attributes
DV_NAME <- 'self$bipartite_rsienaDV'
#
steps_per_actor <- 501. NO Endogenous Noise in Environment (Structural Effects = 0)
1.1 Environment Configuration
#
environ_params <- list(
M = 12, ## Actors
N = 12, ## Components
BI_PROB = 1, ## Environmental Density (DGP hyperparameter)
component_matrix_start = 'rand', ##**TODO** Implement: 'rand','modular','semi-modular',...
rand_seed = 1234,
plot_init = F,
name = '_test_tutorial_nb_'
)
#
env1 <- SaomNkRSienaBiEnv$new(environ_params)##
## TEST FROM CALLED CLASS INIT *BEFORE* BASE INIT
##
## CALLED _BASE_ INIT
##
## TEST FROM CALLED CLASS INIT *AFTER* BASE INITprint(c(env1$M, env1$N))## [1] 12 12library(Matrix) # For block diagonal matrices
#
create_block_diag <- function(N, B) {
# Determine the approximate block size
block_sizes <- rep(N %/% B, B) # Equal-sized blocks
block_sizes[1:(N %% B)] <- if (N %% B == 0) {
block_sizes[1:(N %% B)] # Adjust for remainder
} else {
block_sizes[1:(N %% B)] + 1
}
# Create individual binary blocks
blocks <- lapply(block_sizes, function(s) matrix(1, nrow = s, ncol = s))
# Combine blocks into a block diagonal matrix
block_diag_matrix <- as.matrix(bdiag(blocks)) # Convert sparse to standard matrix
# Ensure exact NxN dimensions
block_diag_matrix <- block_diag_matrix[1:N, 1:N] # Trim to N x N
# return
return(block_diag_matrix)
}
# Example usage:
N <- 6 # Number of rows/columns
B <- 2 # Number of blocks
block_matrix <- create_block_diag(8, 2)
# Print the matrix
print(block_matrix)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 1 1 1 0 0 0 0
## [2,] 1 1 1 1 0 0 0 0
## [3,] 1 1 1 1 0 0 0 0
## [4,] 1 1 1 1 0 0 0 0
## [5,] 0 0 0 0 1 1 1 1
## [6,] 0 0 0 0 1 1 1 1
## [7,] 0 0 0 0 1 1 1 1
## [8,] 0 0 0 0 1 1 1 11.2 Structrual Model
SAOM Objective Function serves as the stochastic actor’s utility function for strategic search.
#
strategies <- list(
egoX = c(-1, 0, 1),
inPopX = c( 1, 0, -1)
)
## 2.b. Component Payoffs vector
set.seed(12345)
component_payoffs <- runif(environ_params$N, min = 0, max = 1)
## 2. Strategies sets the objective function as a linear combination of network stats across DVs
#
actor_strats_list <- lapply(strategies, function(strat) rep(strat, environ_params$M/length(strat)) )
component_int_mat <- create_block_diag(environ_params$N, round(environ_params$N/3))
# dyad_cov_XWX <- ( outer(component_payoffs, component_payoffs, '*') *
# create_block_diag(environ_params$N, round(environ_params$N/3))
# )
dyad_cov_X <- matrix(runif(n = environ_params$N*environ_params$M, min = -.5, max= 1), nrow=environ_params$M)
# dyad_cov_X <- matrix(runif(environ_params$N*environ_params$M) - runif(environ_params$N*environ_params$M),
# nrow=environ_params$M)
# dyad_cov_XWX_X <- t( dyad_cov_XWX %*% t(dyad_cov_X) )
#
structure_model <- list(
dv_bipartite = list(
name = 'self$bipartite_rsienaDV',
effects = list( ##**STRUCTURAL EFFECTS -- dyadic/network endogeneity sources**
list(effect='density', parameter= 0, dv_name=DV_NAME, fix=T ), ##interaction1 = NULL
list(effect='inPop', parameter= 0, dv_name=DV_NAME, fix=T ), #interaction1 = NUL
list(effect='outAct', parameter= 0, dv_name=DV_NAME, fix=T )
),
## COVARIATE EFFECTS
coCovars = list(
##** COMPONENTS : MONADIC CONSTANT COVARIATE EFFECTS **##
# list(effect='altX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
# list(effect='outActX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
##** STRATEGIES : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='egoX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar', x = actor_strats_list[[1]]
), #interaction1 = NULL
list(effect='inPopX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar', x = actor_strats_list[[2]]
) ,
list(effect='totInDist2', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_3_coCovar', x = (actor_strats_list[[1]] - actor_strats_list[[2]] )
)
),
##**MONADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC STRATEGY PROGRAMS**
varCovars = list(),
##**DYADIC CONSTANT COVARIATE EFFECTS -- EXOGENOUS INTERACTION MATRIX**
coDyadCovars = list(
list(effect='XWX', parameter= .1, dv_name=DV_NAME, fix=T, nodeSet=c('COMPONENTS','COMPONENTS'),##if M=N, must provide nodeSet
interaction1='self$component_1_coDyadCovar',
x = component_int_mat ## component-[actor]-component dyads
) ,
list(effect='X', parameter= 0, dv_name=DV_NAME, fix=T, nodeSet=c('ACTORS','COMPONENTS'), ##if M=N, must provide nodeSet
interaction1='self$component_2_coDyadCovar',
x = dyad_cov_X ## deltas = changes of payoff contributions from each actor-component
)
),
##**DYADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC INTERACTION MATRIX**
varDyadCovars = list(),
interactions = list(
list(effect='egoX|XWX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar',
interaction2='self$component_1_coDyadCovar'
),
list(effect='inPopX|X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar',
interaction2='self$component_2_coDyadCovar'
),
list(effect='totInDist2|X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_3_coCovar',
interaction2='self$component_2_coDyadCovar'
)
)
)
)env1$preview_effects(structure_model, filter=FALSE)## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html .# ## Uncomment for HTML output with filterable data table
# env1$preview_effects(structure_model, filter=TRUE)1.3 Run RSiena Search Process
## TODO: PICK UP WITH coDydCovar Interation Matrix
## Run Rsiena search using variable parameters in theta_matrix
env1$search_rsiena(
structure_model,
iterations = env1$M * steps_per_actor,
digits = 4,
run_seed = 12345
)##
##
## self$rsiena_data :
##
## Dependent variables: self$bipartite_rsienaDV
## Number of observations: 2
##
## Nodesets ACTORS COMPONENTS
## Number of nodes 12 12
##
## Dependent variable self$bipartite_rsienaDV
## Type bipartite
## Observations 2
## First nodeset ACTORS
## Second nodeset COMPONENTS
## Densities 1 1
##
## Constant covariates: self$strat_1_coCovar, self$strat_2_coCovar, self$strat_3_coCovar
## Constant dyadic covariates: self$component_1_coDyadCovar, self$component_2_coDyadCovar
##
## structural effects i=1, j=1
## $effect
## [1] "density"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 1.60944 0
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=2
## $effect
## [1] "inPop"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=3
## $effect
## [1] "outAct"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=1
## $effect
## [1] "egoX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $x
## [1] -1 0 1 -1 0 1 -1 0 1 -1 0 1
##
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=2
## $effect
## [1] "inPopX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $x
## [1] 1 0 -1 1 0 -1 1 0 -1 1 0 -1
##
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 1
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 0
##
## coCovars i=1, j=3
## $effect
## [1] "totInDist2"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_3_coCovar"
##
## $x
## [1] -2 0 2 -2 0 2 -2 0 2 -2 0 2
##
## effectName include fix test initialValue parm
## 1 self$strat_3_coCovar tot in-alter dist 2 TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_3_coCovar tot in-alter dist 2 TRUE TRUE FALSE 0 0
##
## coDyadCovars i=1, j=1
## $effect
## [1] "XWX"
##
## $parameter
## [1] 0.1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $nodeSet
## [1] "COMPONENTS" "COMPONENTS"
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1 1 1 0 0 0 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1 0 0 0
## [8,] 0 0 0 0 0 0 1 1 1 0 0 0
## [9,] 0 0 0 0 0 0 1 1 1 0 0 0
## [10,] 0 0 0 0 0 0 0 0 0 1 1 1
## [11,] 0 0 0 0 0 0 0 0 0 1 1 1
## [12,] 0 0 0 0 0 0 0 0 0 1 1 1
##
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 0
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 0.1
##
## coDyadCovars i=1, j=2
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $nodeSet
## [1] "ACTORS" "COMPONENTS"
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.603527429 0.46681395 0.80319235 -0.41741927 0.6873517 0.059404367
## [2,] -0.498295120 0.08474273 0.85623200 0.43831420 -0.1119735 0.003707558
## [3,] 0.086805003 0.54781546 0.42613685 0.94670543 0.9789757 -0.427622971
## [4,] 0.193741981 0.31608680 -0.29895255 0.74095430 0.6353106 0.428421309
## [5,] 0.082215972 -0.16029923 0.67328992 -0.02745765 0.9696674 0.942170938
## [6,] 0.103727713 0.22683663 0.14379823 -0.18046182 -0.1715782 0.482440764
## [7,] -0.231554623 0.68951075 0.89091096 0.59874418 0.9230608 0.265437987
## [8,] 0.927488131 -0.49101856 0.65986484 0.24886153 -0.2758131 -0.274852684
## [9,] 0.180592110 -0.21843133 -0.11047813 0.59465796 0.4005355 0.805671821
## [10,] -0.009871387 0.52275043 -0.01816299 -0.37949593 0.9196461 0.271662517
## [11,] 0.948122985 0.05515619 -0.40970726 0.15329573 0.5325300 -0.487028130
## [12,] 0.561222816 0.04243836 -0.43481532 -0.14512932 0.2583006 -0.471207852
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] -0.28323215 0.5801748 -0.12332339 0.33918510 0.7191507 -0.27948818
## [2,] -0.04245237 0.6249194 -0.17739638 0.27237634 0.4947419 0.93860569
## [3,] 0.73848529 -0.3565392 0.41421407 -0.36543221 0.9167120 0.98516567
## [4,] 0.25351696 0.0967388 0.07516695 0.52487586 0.8122661 0.24465896
## [5,] 0.70535894 -0.0583019 0.63290657 0.56495807 0.5460568 0.87947147
## [6,] -0.40904003 0.4258805 0.06960436 0.70034733 -0.2224387 0.05819521
## [7,] 0.89193270 0.9614112 0.69245810 0.91611762 -0.3536835 0.74058617
## [8,] 0.71226783 0.4273181 0.85853670 -0.22003079 0.7116400 0.33856442
## [9,] -0.38177999 0.2820538 0.97603926 -0.08203658 0.2826665 0.69350681
## [10,] 0.40139242 0.8537557 0.38192196 0.66139493 0.5403110 0.02803262
## [11,] 0.57216982 0.4561816 -0.48580392 -0.44982858 -0.1755056 -0.18720849
## [12,] 0.27076848 0.7964517 -0.01881190 0.50787721 0.4449143 0.67717094
##
## There is no effect with short name X,
## and with interaction1 = <>, interaction2 = <>, and type = <eval>,
## for dependent variable self$bipartite_rsienaDV .
## See effectsDocumentation() for this effects object.
## effectName include fix test initialValue parm
## 1 self$component_2_coDyadCovar TRUE TRUE FALSE 0 0
##
## interactions i=1, j=1
## $effect
## [1] "egoX|XWX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $interaction2
## [1] "self$component_1_coDyadCovar"
##
## $effects
## [1] "egoX" "XWX"
##
## effectName
## 1 self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 89 81
##
## interactions i=1, j=2
## $effect
## [1] "inPopX|X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $interaction2
## [1] "self$component_2_coDyadCovar"
##
## $effects
## [1] "inPopX" "X"
##
## effectName
## 1 ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 170 86
##
## interactions i=1, j=3
## $effect
## [1] "totInDist2|X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_3_coCovar"
##
## $interaction2
## [1] "self$component_2_coDyadCovar"
##
## $effects
## [1] "totInDist2" "X"
##
## effectName
## 1 self$strat_3_coCovar tot in-alter dist 2 x self$component_2_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 209 86
## [1] "density"
## [1] "inPop"
## [1] "outAct"
## [1] "XWX"
## [1] "X"
## [1] "egoX"
## [1] "inPopX"
## [1] "totInDist2"
## [1] "egoX|XWX"
## [1] "inPopX|X"
## [1] "totInDist2|X"
##
##
## theta_matrix :
##
## density inPop outAct XWX X egoX inPopX totInDist2 egoX|XWX inPopX|X
## [1,] 0 0 0 0.1 0 0 0 0 0 0
## [2,] 0 0 0 0.1 0 0 0 0 0 0
## [3,] 0 0 0 0.1 0 0 0 0 0 0
## [4,] 0 0 0 0.1 0 0 0 0 0 0
## [5,] 0 0 0 0.1 0 0 0 0 0 0
## [6,] 0 0 0 0.1 0 0 0 0 0 0
## [7,] 0 0 0 0.1 0 0 0 0 0 0
## [8,] 0 0 0 0.1 0 0 0 0 0 0
## [9,] 0 0 0 0.1 0 0 0 0 0 0
## [10,] 0 0 0 0.1 0 0 0 0 0 0
## [11,] 0 0 0 0.1 0 0 0 0 0 0
## [12,] 0 0 0 0.1 0 0 0 0 0 0
## [13,] 0 0 0 0.1 0 0 0 0 0 0
## [14,] 0 0 0 0.1 0 0 0 0 0 0
## [15,] 0 0 0 0.1 0 0 0 0 0 0
## [16,] 0 0 0 0.1 0 0 0 0 0 0
## [17,] 0 0 0 0.1 0 0 0 0 0 0
## [18,] 0 0 0 0.1 0 0 0 0 0 0
## [19,] 0 0 0 0.1 0 0 0 0 0 0
## [20,] 0 0 0 0.1 0 0 0 0 0 0
## [21,] 0 0 0 0.1 0 0 0 0 0 0
## [22,] 0 0 0 0.1 0 0 0 0 0 0
## [23,] 0 0 0 0.1 0 0 0 0 0 0
## [24,] 0 0 0 0.1 0 0 0 0 0 0
## [25,] 0 0 0 0.1 0 0 0 0 0 0
## [26,] 0 0 0 0.1 0 0 0 0 0 0
## [27,] 0 0 0 0.1 0 0 0 0 0 0
## [28,] 0 0 0 0.1 0 0 0 0 0 0
## [29,] 0 0 0 0.1 0 0 0 0 0 0
## [30,] 0 0 0 0.1 0 0 0 0 0 0
## [31,] 0 0 0 0.1 0 0 0 0 0 0
## [32,] 0 0 0 0.1 0 0 0 0 0 0
## [33,] 0 0 0 0.1 0 0 0 0 0 0
## [34,] 0 0 0 0.1 0 0 0 0 0 0
## [35,] 0 0 0 0.1 0 0 0 0 0 0
## [36,] 0 0 0 0.1 0 0 0 0 0 0
## [37,] 0 0 0 0.1 0 0 0 0 0 0
## [38,] 0 0 0 0.1 0 0 0 0 0 0
## [39,] 0 0 0 0.1 0 0 0 0 0 0
## [40,] 0 0 0 0.1 0 0 0 0 0 0
## [41,] 0 0 0 0.1 0 0 0 0 0 0
## [42,] 0 0 0 0.1 0 0 0 0 0 0
## [43,] 0 0 0 0.1 0 0 0 0 0 0
## [44,] 0 0 0 0.1 0 0 0 0 0 0
## [45,] 0 0 0 0.1 0 0 0 0 0 0
## [46,] 0 0 0 0.1 0 0 0 0 0 0
## [47,] 0 0 0 0.1 0 0 0 0 0 0
## [48,] 0 0 0 0.1 0 0 0 0 0 0
## [49,] 0 0 0 0.1 0 0 0 0 0 0
## [50,] 0 0 0 0.1 0 0 0 0 0 0
## [51,] 0 0 0 0.1 0 0 0 0 0 0
## [52,] 0 0 0 0.1 0 0 0 0 0 0
## [53,] 0 0 0 0.1 0 0 0 0 0 0
## [54,] 0 0 0 0.1 0 0 0 0 0 0
## [55,] 0 0 0 0.1 0 0 0 0 0 0
## [56,] 0 0 0 0.1 0 0 0 0 0 0
## [57,] 0 0 0 0.1 0 0 0 0 0 0
## [58,] 0 0 0 0.1 0 0 0 0 0 0
## [59,] 0 0 0 0.1 0 0 0 0 0 0
## [60,] 0 0 0 0.1 0 0 0 0 0 0
## [61,] 0 0 0 0.1 0 0 0 0 0 0
## [62,] 0 0 0 0.1 0 0 0 0 0 0
## [63,] 0 0 0 0.1 0 0 0 0 0 0
## [64,] 0 0 0 0.1 0 0 0 0 0 0
## [65,] 0 0 0 0.1 0 0 0 0 0 0
## [66,] 0 0 0 0.1 0 0 0 0 0 0
## [67,] 0 0 0 0.1 0 0 0 0 0 0
## [68,] 0 0 0 0.1 0 0 0 0 0 0
## [69,] 0 0 0 0.1 0 0 0 0 0 0
## [70,] 0 0 0 0.1 0 0 0 0 0 0
## [71,] 0 0 0 0.1 0 0 0 0 0 0
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## [503,] 0 0 0 0.1 0 0 0 0 0 0
## [504,] 0 0 0 0.1 0 0 0 0 0 0
## [505,] 0 0 0 0.1 0 0 0 0 0 0
## [506,] 0 0 0 0.1 0 0 0 0 0 0
## [507,] 0 0 0 0.1 0 0 0 0 0 0
## [508,] 0 0 0 0.1 0 0 0 0 0 0
## [509,] 0 0 0 0.1 0 0 0 0 0 0
## [510,] 0 0 0 0.1 0 0 0 0 0 0
## [511,] 0 0 0 0.1 0 0 0 0 0 0
## [512,] 0 0 0 0.1 0 0 0 0 0 0
## [513,] 0 0 0 0.1 0 0 0 0 0 0
## [514,] 0 0 0 0.1 0 0 0 0 0 0
## [515,] 0 0 0 0.1 0 0 0 0 0 0
## [516,] 0 0 0 0.1 0 0 0 0 0 0
## [517,] 0 0 0 0.1 0 0 0 0 0 0
## [518,] 0 0 0 0.1 0 0 0 0 0 0
## [519,] 0 0 0 0.1 0 0 0 0 0 0
## [520,] 0 0 0 0.1 0 0 0 0 0 0
## [521,] 0 0 0 0.1 0 0 0 0 0 0
## [522,] 0 0 0 0.1 0 0 0 0 0 0
## [523,] 0 0 0 0.1 0 0 0 0 0 0
## [524,] 0 0 0 0.1 0 0 0 0 0 0
## [525,] 0 0 0 0.1 0 0 0 0 0 0
## [526,] 0 0 0 0.1 0 0 0 0 0 0
## [527,] 0 0 0 0.1 0 0 0 0 0 0
## [528,] 0 0 0 0.1 0 0 0 0 0 0
## [529,] 0 0 0 0.1 0 0 0 0 0 0
## [530,] 0 0 0 0.1 0 0 0 0 0 0
## [531,] 0 0 0 0.1 0 0 0 0 0 0
## [532,] 0 0 0 0.1 0 0 0 0 0 0
## [533,] 0 0 0 0.1 0 0 0 0 0 0
## [534,] 0 0 0 0.1 0 0 0 0 0 0
## [535,] 0 0 0 0.1 0 0 0 0 0 0
## [536,] 0 0 0 0.1 0 0 0 0 0 0
## [537,] 0 0 0 0.1 0 0 0 0 0 0
## [538,] 0 0 0 0.1 0 0 0 0 0 0
## [539,] 0 0 0 0.1 0 0 0 0 0 0
## [540,] 0 0 0 0.1 0 0 0 0 0 0
## [541,] 0 0 0 0.1 0 0 0 0 0 0
## [542,] 0 0 0 0.1 0 0 0 0 0 0
## [543,] 0 0 0 0.1 0 0 0 0 0 0
## [544,] 0 0 0 0.1 0 0 0 0 0 0
## [545,] 0 0 0 0.1 0 0 0 0 0 0
## [546,] 0 0 0 0.1 0 0 0 0 0 0
## [547,] 0 0 0 0.1 0 0 0 0 0 0
## [548,] 0 0 0 0.1 0 0 0 0 0 0
## [549,] 0 0 0 0.1 0 0 0 0 0 0
## [550,] 0 0 0 0.1 0 0 0 0 0 0
## [551,] 0 0 0 0.1 0 0 0 0 0 0
## [552,] 0 0 0 0.1 0 0 0 0 0 0
## [553,] 0 0 0 0.1 0 0 0 0 0 0
## [554,] 0 0 0 0.1 0 0 0 0 0 0
## [555,] 0 0 0 0.1 0 0 0 0 0 0
## [556,] 0 0 0 0.1 0 0 0 0 0 0
## [557,] 0 0 0 0.1 0 0 0 0 0 0
## [558,] 0 0 0 0.1 0 0 0 0 0 0
## [559,] 0 0 0 0.1 0 0 0 0 0 0
## [560,] 0 0 0 0.1 0 0 0 0 0 0
## [561,] 0 0 0 0.1 0 0 0 0 0 0
## [562,] 0 0 0 0.1 0 0 0 0 0 0
## [563,] 0 0 0 0.1 0 0 0 0 0 0
## [564,] 0 0 0 0.1 0 0 0 0 0 0
## [565,] 0 0 0 0.1 0 0 0 0 0 0
## [566,] 0 0 0 0.1 0 0 0 0 0 0
## [567,] 0 0 0 0.1 0 0 0 0 0 0
## [568,] 0 0 0 0.1 0 0 0 0 0 0
## [569,] 0 0 0 0.1 0 0 0 0 0 0
## [570,] 0 0 0 0.1 0 0 0 0 0 0
## [571,] 0 0 0 0.1 0 0 0 0 0 0
## [572,] 0 0 0 0.1 0 0 0 0 0 0
## [573,] 0 0 0 0.1 0 0 0 0 0 0
## [574,] 0 0 0 0.1 0 0 0 0 0 0
## [575,] 0 0 0 0.1 0 0 0 0 0 0
## [576,] 0 0 0 0.1 0 0 0 0 0 0
## [577,] 0 0 0 0.1 0 0 0 0 0 0
## [578,] 0 0 0 0.1 0 0 0 0 0 0
## [579,] 0 0 0 0.1 0 0 0 0 0 0
## [580,] 0 0 0 0.1 0 0 0 0 0 0
## [581,] 0 0 0 0.1 0 0 0 0 0 0
## [582,] 0 0 0 0.1 0 0 0 0 0 0
## [583,] 0 0 0 0.1 0 0 0 0 0 0
## [584,] 0 0 0 0.1 0 0 0 0 0 0
## [585,] 0 0 0 0.1 0 0 0 0 0 0
## [586,] 0 0 0 0.1 0 0 0 0 0 0
## [587,] 0 0 0 0.1 0 0 0 0 0 0
## [588,] 0 0 0 0.1 0 0 0 0 0 0
## [589,] 0 0 0 0.1 0 0 0 0 0 0
## [590,] 0 0 0 0.1 0 0 0 0 0 0
## [591,] 0 0 0 0.1 0 0 0 0 0 0
## [592,] 0 0 0 0.1 0 0 0 0 0 0
## [593,] 0 0 0 0.1 0 0 0 0 0 0
## [594,] 0 0 0 0.1 0 0 0 0 0 0
## [595,] 0 0 0 0.1 0 0 0 0 0 0
## [596,] 0 0 0 0.1 0 0 0 0 0 0
## [597,] 0 0 0 0.1 0 0 0 0 0 0
## [598,] 0 0 0 0.1 0 0 0 0 0 0
## [599,] 0 0 0 0.1 0 0 0 0 0 0
## [600,] 0 0 0 0.1 0 0 0 0 0 0
## totInDist2|X
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
## [8,] 0
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## [13,] 0
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## [481,] 0
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## [485,] 0
## [486,] 0
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## [491,] 0
## [492,] 0
## [493,] 0
## [494,] 0
## [495,] 0
## [496,] 0
## [497,] 0
## [498,] 0
## [499,] 0
## [500,] 0
## [501,] 0
## [502,] 0
## [503,] 0
## [504,] 0
## [505,] 0
## [506,] 0
## [507,] 0
## [508,] 0
## [509,] 0
## [510,] 0
## [511,] 0
## [512,] 0
## [513,] 0
## [514,] 0
## [515,] 0
## [516,] 0
## [517,] 0
## [518,] 0
## [519,] 0
## [520,] 0
## [521,] 0
## [522,] 0
## [523,] 0
## [524,] 0
## [525,] 0
## [526,] 0
## [527,] 0
## [528,] 0
## [529,] 0
## [530,] 0
## [531,] 0
## [532,] 0
## [533,] 0
## [534,] 0
## [535,] 0
## [536,] 0
## [537,] 0
## [538,] 0
## [539,] 0
## [540,] 0
## [541,] 0
## [542,] 0
## [543,] 0
## [544,] 0
## [545,] 0
## [546,] 0
## [547,] 0
## [548,] 0
## [549,] 0
## [550,] 0
## [551,] 0
## [552,] 0
## [553,] 0
## [554,] 0
## [555,] 0
## [556,] 0
## [557,] 0
## [558,] 0
## [559,] 0
## [560,] 0
## [561,] 0
## [562,] 0
## [563,] 0
## [564,] 0
## [565,] 0
## [566,] 0
## [567,] 0
## [568,] 0
## [569,] 0
## [570,] 0
## [571,] 0
## [572,] 0
## [573,] 0
## [574,] 0
## [575,] 0
## [576,] 0
## [577,] 0
## [578,] 0
## [579,] 0
## [580,] 0
## [581,] 0
## [582,] 0
## [583,] 0
## [584,] 0
## [585,] 0
## [586,] 0
## [587,] 0
## [588,] 0
## [589,] 0
## [590,] 0
## [591,] 0
## [592,] 0
## [593,] 0
## [594,] 0
## [595,] 0
## [596,] 0
## [597,] 0
## [598,] 0
## [599,] 0
## [600,] 0
## If you use this algorithm object, siena07 will create/use an output file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_test_tutorial_nb__173939300521.txt .
##
## Start phase 0
## theta: 0 0 0 0 0 0 0 0 0 0 0
##
## Start phase 3
## Phase 3 Iteration 100 Progress 17%
## Phase 3 Iteration 200 Progress 33%
## Phase 3 Iteration 300 Progress 50%
## Phase 3 Iteration 400 Progress 67%
## Phase 3 Iteration 500 Progress 83%
## Phase 3 Iteration 600 Progress 100%
## Parameter values used for simulations
##
## Mean Standard
## value Deviation
##
## Rate parameters:
## 0 Rate parameter NA ( NA )
##
## Other parameters:
## 1. eval outdegree (density) 0.0 ( NA )
## 2. eval indegree - popularity 0.0 ( NA )
## 3. eval outdegree - activity 0.0 ( NA )
## 4. eval XW=>X closure of self$component_1_coDyadCovar 0.1 ( NA )
## 5. eval self$component_2_coDyadCovar 0.0 ( NA )
## 6. eval self$strat_1_coCovar ego 0.0 ( NA )
## 7. eval ind. pop.^(1/0) weighted self$strat_2_coCovar 0.0 ( NA )
## 8. eval self$strat_3_coCovar tot in-alter dist 2 0.0 ( NA )
## 9. eval self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.0 ( NA )
## 10. eval ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.0 ( NA )
## 11. eval self$strat_3_coCovar tot in-alter dist 2 x self$component_2_coDyadCovar 0.0 ( NA )
##
## Simulated means and standard deviations
## 1. Number of ties 143.118 0.323
## 2. Sum of squared indegrees 1707.722 7.435
## 3. Sum of squared outdegrees 1707.722 7.435
## 4. XW=>X closure of self$component_1_coDyadCovar 284.473 1.293
## 5. Sum of ties x self$component_2_coDyadCovar 0.024 0.416
## 6. Sum of outdegrees x self$strat_1_coCovar 0.018 0.772
## 7. indegree pop.^(1/0) weighted self$strat_2_coCovar -0.202 8.490
## 8. self$strat_3_coCovar tot in-alter dist 2 0.367 15.436
## 9. self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.073 3.087
## 10. ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.054 0.945
## 11. self$strat_3_coCovar tot in-alter dist 2 x self$component_2_coDyadCovar 12.445 2.102
##
##
## Simulated statistics are in x$sf
## and simulated dependent variables in x$sims, where x is the created object.
##
## Total of 600 iteration steps.
##
## Covariance matrix of estimates (correlations below diagonal)
##
## 0 0 0 0 0 0 0 0 0 0 0
## NaN 0 0 0 0 0 0 0 0 0 0
## NaN NaN 0 0 0 0 0 0 0 0 0
## NaN NaN NaN 0 0 0 0 0 0 0 0
## NaN NaN NaN NaN 0 0 0 0 0 0 0
## NaN NaN NaN NaN NaN 0 0 0 0 0 0
## NaN NaN NaN NaN NaN NaN 0 0 0 0 0
## NaN NaN NaN NaN NaN NaN NaN 0 0 0 0
## NaN NaN NaN NaN NaN NaN NaN NaN 0 0 0
## NaN NaN NaN NaN NaN NaN NaN NaN NaN 0 0
## NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0
##
## Derivative matrix of expected statistics X by parameters:
##
## 0.104 2.400 2.400 0.417 -0.003 -0.002 0.024 0.000 -0.009 -0.006 0.020
## 1.252 28.795 28.795 5.008 -0.034 -0.026 0.286 0.000 -0.104 -0.077 0.236
## 2.400 55.191 55.191 9.598 -0.064 -0.050 0.549 0.000 -0.200 -0.148 0.453
## 0.417 9.598 9.598 1.669 -0.011 -0.009 0.095 0.000 -0.035 -0.026 0.079
## -0.002 -0.041 -0.041 -0.007 0.155 0.006 -0.064 0.000 0.023 0.012 -0.049
## -0.005 -0.126 -0.126 -0.022 -0.003 0.066 -0.724 0.000 0.263 0.010 -0.023
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000
## -0.022 -0.503 -0.503 -0.087 -0.012 0.263 -2.898 0.000 1.054 0.040 -0.091
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.006 0.130 0.130 0.023 -0.028 -0.010 0.105 0.000 -0.038 -0.078 0.551
##
## Covariance matrix of X (correlations below diagonal):
##
## 0.105 2.404 2.404 0.418 -0.003 -0.002 0.024 -0.043 -0.009 -0.006 0.020
## 1.000 55.283 55.283 9.614 -0.064 -0.050 0.550 -1.000 -0.200 -0.148 0.453
## 1.000 1.000 55.283 9.614 -0.064 -0.050 0.550 -1.000 -0.200 -0.148 0.453
## 1.000 1.000 1.000 1.672 -0.011 -0.009 0.096 -0.174 -0.035 -0.026 0.079
## -0.021 -0.021 -0.021 -0.021 0.173 -0.029 0.323 -0.587 -0.117 0.005 -0.036
## -0.009 -0.009 -0.009 -0.009 -0.091 0.596 -6.552 11.913 2.383 0.088 -0.197
## 0.009 0.009 0.009 0.009 0.091 -1.000 72.074 -131.044 -26.209 -0.969 2.166
## -0.009 -0.009 -0.009 -0.009 -0.091 1.000 -1.000 238.263 47.653 1.763 -3.938
## -0.009 -0.009 -0.009 -0.009 -0.091 1.000 -1.000 1.000 9.531 0.353 -0.788
## -0.021 -0.021 -0.021 -0.021 0.013 0.121 -0.121 0.121 0.121 0.892 -1.892
## 0.029 0.029 0.029 0.029 -0.042 -0.121 0.121 -0.121 -0.121 -0.953 4.420
##
##
##
## Simulated Decision Chain Summary:
##
## dv_type dv_type_bin dv_varname id_from
## Length:600 Min. :0 Length:600 Min. : 1.000
## Class :character 1st Qu.:0 Class :character 1st Qu.: 4.000
## Mode :character Median :0 Mode :character Median : 7.000
## Mean :0 Mean : 6.665
## 3rd Qu.:0 3rd Qu.:10.000
## Max. :0 Max. :12.000
## id_to beh_difference reciprocal_rate LogOptionSetProb
## Min. : 1.000 Min. :0 Min. :0.08333 Min. :-2.485
## 1st Qu.: 4.000 1st Qu.:0 1st Qu.:0.08333 1st Qu.:-2.485
## Median : 7.000 Median :0 Median :0.08333 Median :-2.485
## Mean : 7.245 Mean :0 Mean :0.08333 Mean :-2.485
## 3rd Qu.:11.000 3rd Qu.:0 3rd Qu.:0.08333 3rd Qu.:-2.485
## Max. :13.000 Max. :0 Max. :0.08333 Max. :-2.485
## LogChoiceProb diagonal stability tie_change
## Min. :-2.602 Length:600 Mode :logical Mode :logical
## 1st Qu.:-2.602 Class :character FALSE:529 FALSE:71
## Median :-2.602 Mode :character TRUE :71 TRUE :529
## Mean :-2.555
## 3rd Qu.:-2.602
## Max. :-2.202
## chain_step_id
## Min. : 1.0
## 1st Qu.:150.8
## Median :300.5
## Mean :300.4
## 3rd Qu.:450.2
## Max. :600.0
## [1] 600 13
## dv_type dv_type_bin dv_varname id_from id_to beh_difference
## 1 Network 0 self$bipartite_rsienaDV 11 11 0
## 2 Network 0 self$bipartite_rsienaDV 4 7 0
## 3 Network 0 self$bipartite_rsienaDV 12 1 0
## 4 Network 0 self$bipartite_rsienaDV 9 1 0
## 5 Network 0 self$bipartite_rsienaDV 6 6 0
## 6 Network 0 self$bipartite_rsienaDV 3 13 0
## 7 Network 0 self$bipartite_rsienaDV 9 9 0
## 8 Network 0 self$bipartite_rsienaDV 9 8 0
## 9 Network 0 self$bipartite_rsienaDV 1 3 0
## 10 Network 0 self$bipartite_rsienaDV 5 5 0
## 11 Network 0 self$bipartite_rsienaDV 2 11 0
## 12 Network 0 self$bipartite_rsienaDV 4 5 0
## 13 Network 0 self$bipartite_rsienaDV 8 13 0
## 14 Network 0 self$bipartite_rsienaDV 4 3 0
## 15 Network 0 self$bipartite_rsienaDV 6 10 0
## 16 Network 0 self$bipartite_rsienaDV 6 4 0
## 17 Network 0 self$bipartite_rsienaDV 4 13 0
## 18 Network 0 self$bipartite_rsienaDV 12 3 0
## 19 Network 0 self$bipartite_rsienaDV 12 10 0
## 20 Network 0 self$bipartite_rsienaDV 5 5 0
## 21 Network 0 self$bipartite_rsienaDV 8 13 0
## 22 Network 0 self$bipartite_rsienaDV 7 3 0
## 23 Network 0 self$bipartite_rsienaDV 1 2 0
## 24 Network 0 self$bipartite_rsienaDV 10 7 0
## 25 Network 0 self$bipartite_rsienaDV 1 13 0
## 26 Network 0 self$bipartite_rsienaDV 1 9 0
## 27 Network 0 self$bipartite_rsienaDV 7 10 0
## 28 Network 0 self$bipartite_rsienaDV 2 6 0
## 29 Network 0 self$bipartite_rsienaDV 8 13 0
## 30 Network 0 self$bipartite_rsienaDV 7 13 0
## 31 Network 0 self$bipartite_rsienaDV 11 4 0
## 32 Network 0 self$bipartite_rsienaDV 10 6 0
## 33 Network 0 self$bipartite_rsienaDV 11 13 0
## 34 Network 0 self$bipartite_rsienaDV 1 5 0
## 35 Network 0 self$bipartite_rsienaDV 7 2 0
## 36 Network 0 self$bipartite_rsienaDV 9 11 0
## 37 Network 0 self$bipartite_rsienaDV 10 1 0
## 38 Network 0 self$bipartite_rsienaDV 10 9 0
## 39 Network 0 self$bipartite_rsienaDV 3 2 0
## 40 Network 0 self$bipartite_rsienaDV 7 10 0
## 41 Network 0 self$bipartite_rsienaDV 12 13 0
## 42 Network 0 self$bipartite_rsienaDV 7 11 0
## 43 Network 0 self$bipartite_rsienaDV 3 11 0
## 44 Network 0 self$bipartite_rsienaDV 6 12 0
## 45 Network 0 self$bipartite_rsienaDV 9 9 0
## 46 Network 0 self$bipartite_rsienaDV 6 13 0
## 47 Network 0 self$bipartite_rsienaDV 11 6 0
## 48 Network 0 self$bipartite_rsienaDV 5 11 0
## 49 Network 0 self$bipartite_rsienaDV 12 11 0
## 50 Network 0 self$bipartite_rsienaDV 3 6 0
## 51 Network 0 self$bipartite_rsienaDV 4 13 0
## 52 Network 0 self$bipartite_rsienaDV 5 13 0
## 53 Network 0 self$bipartite_rsienaDV 2 11 0
## 54 Network 0 self$bipartite_rsienaDV 12 1 0
## 55 Network 0 self$bipartite_rsienaDV 10 4 0
## 56 Network 0 self$bipartite_rsienaDV 2 7 0
## 57 Network 0 self$bipartite_rsienaDV 10 2 0
## 58 Network 0 self$bipartite_rsienaDV 7 9 0
## 59 Network 0 self$bipartite_rsienaDV 9 6 0
## 60 Network 0 self$bipartite_rsienaDV 12 6 0
## 61 Network 0 self$bipartite_rsienaDV 7 9 0
## 62 Network 0 self$bipartite_rsienaDV 2 2 0
## 63 Network 0 self$bipartite_rsienaDV 7 13 0
## 64 Network 0 self$bipartite_rsienaDV 11 6 0
## 65 Network 0 self$bipartite_rsienaDV 7 7 0
## 66 Network 0 self$bipartite_rsienaDV 9 7 0
## 67 Network 0 self$bipartite_rsienaDV 12 13 0
## 68 Network 0 self$bipartite_rsienaDV 9 13 0
## 69 Network 0 self$bipartite_rsienaDV 5 11 0
## 70 Network 0 self$bipartite_rsienaDV 11 12 0
## 71 Network 0 self$bipartite_rsienaDV 3 2 0
## 72 Network 0 self$bipartite_rsienaDV 10 13 0
## 73 Network 0 self$bipartite_rsienaDV 1 2 0
## 74 Network 0 self$bipartite_rsienaDV 12 10 0
## 75 Network 0 self$bipartite_rsienaDV 4 3 0
## 76 Network 0 self$bipartite_rsienaDV 5 12 0
## 77 Network 0 self$bipartite_rsienaDV 10 2 0
## 78 Network 0 self$bipartite_rsienaDV 4 6 0
## 79 Network 0 self$bipartite_rsienaDV 10 7 0
## 80 Network 0 self$bipartite_rsienaDV 9 12 0
## 81 Network 0 self$bipartite_rsienaDV 10 3 0
## 82 Network 0 self$bipartite_rsienaDV 1 1 0
## 83 Network 0 self$bipartite_rsienaDV 9 9 0
## 84 Network 0 self$bipartite_rsienaDV 9 3 0
## 85 Network 0 self$bipartite_rsienaDV 5 6 0
## 86 Network 0 self$bipartite_rsienaDV 11 8 0
## 87 Network 0 self$bipartite_rsienaDV 4 12 0
## 88 Network 0 self$bipartite_rsienaDV 9 9 0
## 89 Network 0 self$bipartite_rsienaDV 5 13 0
## 90 Network 0 self$bipartite_rsienaDV 1 3 0
## 91 Network 0 self$bipartite_rsienaDV 9 10 0
## 92 Network 0 self$bipartite_rsienaDV 9 7 0
## 93 Network 0 self$bipartite_rsienaDV 11 12 0
## 94 Network 0 self$bipartite_rsienaDV 8 11 0
## 95 Network 0 self$bipartite_rsienaDV 9 9 0
## 96 Network 0 self$bipartite_rsienaDV 8 8 0
## 97 Network 0 self$bipartite_rsienaDV 10 3 0
## 98 Network 0 self$bipartite_rsienaDV 6 4 0
## 99 Network 0 self$bipartite_rsienaDV 9 12 0
## 100 Network 0 self$bipartite_rsienaDV 10 8 0
## 101 Network 0 self$bipartite_rsienaDV 10 13 0
## 102 Network 0 self$bipartite_rsienaDV 8 6 0
## 103 Network 0 self$bipartite_rsienaDV 6 10 0
## 104 Network 0 self$bipartite_rsienaDV 4 10 0
## 105 Network 0 self$bipartite_rsienaDV 11 9 0
## 106 Network 0 self$bipartite_rsienaDV 11 1 0
## 107 Network 0 self$bipartite_rsienaDV 11 11 0
## 108 Network 0 self$bipartite_rsienaDV 4 3 0
## 109 Network 0 self$bipartite_rsienaDV 5 7 0
## 110 Network 0 self$bipartite_rsienaDV 1 7 0
## 111 Network 0 self$bipartite_rsienaDV 3 9 0
## 112 Network 0 self$bipartite_rsienaDV 11 4 0
## 113 Network 0 self$bipartite_rsienaDV 1 8 0
## 114 Network 0 self$bipartite_rsienaDV 1 1 0
## 115 Network 0 self$bipartite_rsienaDV 10 8 0
## 116 Network 0 self$bipartite_rsienaDV 6 2 0
## 117 Network 0 self$bipartite_rsienaDV 4 3 0
## 118 Network 0 self$bipartite_rsienaDV 5 12 0
## 119 Network 0 self$bipartite_rsienaDV 8 1 0
## 120 Network 0 self$bipartite_rsienaDV 4 1 0
## 121 Network 0 self$bipartite_rsienaDV 1 12 0
## 122 Network 0 self$bipartite_rsienaDV 2 11 0
## 123 Network 0 self$bipartite_rsienaDV 1 10 0
## 124 Network 0 self$bipartite_rsienaDV 1 13 0
## 125 Network 0 self$bipartite_rsienaDV 10 7 0
## 126 Network 0 self$bipartite_rsienaDV 12 4 0
## 127 Network 0 self$bipartite_rsienaDV 2 8 0
## 128 Network 0 self$bipartite_rsienaDV 7 6 0
## 129 Network 0 self$bipartite_rsienaDV 5 11 0
## 130 Network 0 self$bipartite_rsienaDV 4 6 0
## 131 Network 0 self$bipartite_rsienaDV 8 4 0
## 132 Network 0 self$bipartite_rsienaDV 9 11 0
## 133 Network 0 self$bipartite_rsienaDV 2 4 0
## 134 Network 0 self$bipartite_rsienaDV 4 8 0
## 135 Network 0 self$bipartite_rsienaDV 8 2 0
## 136 Network 0 self$bipartite_rsienaDV 10 12 0
## 137 Network 0 self$bipartite_rsienaDV 10 11 0
## 138 Network 0 self$bipartite_rsienaDV 11 13 0
## 139 Network 0 self$bipartite_rsienaDV 11 2 0
## 140 Network 0 self$bipartite_rsienaDV 5 11 0
## 141 Network 0 self$bipartite_rsienaDV 5 5 0
## 142 Network 0 self$bipartite_rsienaDV 4 10 0
## 143 Network 0 self$bipartite_rsienaDV 10 11 0
## 144 Network 0 self$bipartite_rsienaDV 11 9 0
## 145 Network 0 self$bipartite_rsienaDV 7 13 0
## 146 Network 0 self$bipartite_rsienaDV 6 5 0
## 147 Network 0 self$bipartite_rsienaDV 8 10 0
## 148 Network 0 self$bipartite_rsienaDV 2 13 0
## 149 Network 0 self$bipartite_rsienaDV 5 4 0
## 150 Network 0 self$bipartite_rsienaDV 3 8 0
## 151 Network 0 self$bipartite_rsienaDV 7 1 0
## 152 Network 0 self$bipartite_rsienaDV 8 11 0
## 153 Network 0 self$bipartite_rsienaDV 11 6 0
## 154 Network 0 self$bipartite_rsienaDV 3 2 0
## 155 Network 0 self$bipartite_rsienaDV 5 1 0
## 156 Network 0 self$bipartite_rsienaDV 7 10 0
## 157 Network 0 self$bipartite_rsienaDV 4 13 0
## 158 Network 0 self$bipartite_rsienaDV 9 3 0
## 159 Network 0 self$bipartite_rsienaDV 8 8 0
## 160 Network 0 self$bipartite_rsienaDV 11 4 0
## 161 Network 0 self$bipartite_rsienaDV 7 5 0
## 162 Network 0 self$bipartite_rsienaDV 3 5 0
## 163 Network 0 self$bipartite_rsienaDV 9 3 0
## 164 Network 0 self$bipartite_rsienaDV 7 2 0
## 165 Network 0 self$bipartite_rsienaDV 10 4 0
## 166 Network 0 self$bipartite_rsienaDV 3 13 0
## 167 Network 0 self$bipartite_rsienaDV 10 10 0
## 168 Network 0 self$bipartite_rsienaDV 7 10 0
## 169 Network 0 self$bipartite_rsienaDV 10 12 0
## 170 Network 0 self$bipartite_rsienaDV 7 3 0
## 171 Network 0 self$bipartite_rsienaDV 1 6 0
## 172 Network 0 self$bipartite_rsienaDV 12 3 0
## 173 Network 0 self$bipartite_rsienaDV 11 7 0
## 174 Network 0 self$bipartite_rsienaDV 12 8 0
## 175 Network 0 self$bipartite_rsienaDV 4 1 0
## 176 Network 0 self$bipartite_rsienaDV 6 13 0
## 177 Network 0 self$bipartite_rsienaDV 8 7 0
## 178 Network 0 self$bipartite_rsienaDV 4 12 0
## 179 Network 0 self$bipartite_rsienaDV 9 13 0
## 180 Network 0 self$bipartite_rsienaDV 4 10 0
## 181 Network 0 self$bipartite_rsienaDV 8 13 0
## 182 Network 0 self$bipartite_rsienaDV 2 13 0
## 183 Network 0 self$bipartite_rsienaDV 9 13 0
## 184 Network 0 self$bipartite_rsienaDV 6 11 0
## 185 Network 0 self$bipartite_rsienaDV 8 8 0
## 186 Network 0 self$bipartite_rsienaDV 1 1 0
## 187 Network 0 self$bipartite_rsienaDV 4 10 0
## 188 Network 0 self$bipartite_rsienaDV 12 2 0
## 189 Network 0 self$bipartite_rsienaDV 11 12 0
## 190 Network 0 self$bipartite_rsienaDV 9 12 0
## 191 Network 0 self$bipartite_rsienaDV 11 2 0
## 192 Network 0 self$bipartite_rsienaDV 1 11 0
## 193 Network 0 self$bipartite_rsienaDV 6 9 0
## 194 Network 0 self$bipartite_rsienaDV 6 5 0
## 195 Network 0 self$bipartite_rsienaDV 9 2 0
## 196 Network 0 self$bipartite_rsienaDV 11 5 0
## 197 Network 0 self$bipartite_rsienaDV 12 9 0
## 198 Network 0 self$bipartite_rsienaDV 7 6 0
## 199 Network 0 self$bipartite_rsienaDV 11 3 0
## 200 Network 0 self$bipartite_rsienaDV 5 1 0
## 201 Network 0 self$bipartite_rsienaDV 2 9 0
## 202 Network 0 self$bipartite_rsienaDV 6 2 0
## 203 Network 0 self$bipartite_rsienaDV 7 11 0
## 204 Network 0 self$bipartite_rsienaDV 8 12 0
## 205 Network 0 self$bipartite_rsienaDV 2 6 0
## 206 Network 0 self$bipartite_rsienaDV 12 13 0
## 207 Network 0 self$bipartite_rsienaDV 1 10 0
## 208 Network 0 self$bipartite_rsienaDV 9 13 0
## 209 Network 0 self$bipartite_rsienaDV 6 1 0
## 210 Network 0 self$bipartite_rsienaDV 10 10 0
## 211 Network 0 self$bipartite_rsienaDV 6 8 0
## 212 Network 0 self$bipartite_rsienaDV 12 4 0
## 213 Network 0 self$bipartite_rsienaDV 9 3 0
## 214 Network 0 self$bipartite_rsienaDV 4 13 0
## 215 Network 0 self$bipartite_rsienaDV 2 1 0
## 216 Network 0 self$bipartite_rsienaDV 10 11 0
## 217 Network 0 self$bipartite_rsienaDV 9 9 0
## 218 Network 0 self$bipartite_rsienaDV 11 5 0
## 219 Network 0 self$bipartite_rsienaDV 8 5 0
## 220 Network 0 self$bipartite_rsienaDV 3 10 0
## 221 Network 0 self$bipartite_rsienaDV 6 7 0
## 222 Network 0 self$bipartite_rsienaDV 5 10 0
## 223 Network 0 self$bipartite_rsienaDV 8 2 0
## 224 Network 0 self$bipartite_rsienaDV 2 4 0
## 225 Network 0 self$bipartite_rsienaDV 10 7 0
## 226 Network 0 self$bipartite_rsienaDV 10 2 0
## 227 Network 0 self$bipartite_rsienaDV 2 9 0
## 228 Network 0 self$bipartite_rsienaDV 2 8 0
## 229 Network 0 self$bipartite_rsienaDV 3 1 0
## 230 Network 0 self$bipartite_rsienaDV 3 6 0
## 231 Network 0 self$bipartite_rsienaDV 9 12 0
## 232 Network 0 self$bipartite_rsienaDV 9 4 0
## 233 Network 0 self$bipartite_rsienaDV 7 4 0
## 234 Network 0 self$bipartite_rsienaDV 7 3 0
## 235 Network 0 self$bipartite_rsienaDV 12 13 0
## 236 Network 0 self$bipartite_rsienaDV 3 5 0
## 237 Network 0 self$bipartite_rsienaDV 8 3 0
## 238 Network 0 self$bipartite_rsienaDV 6 11 0
## 239 Network 0 self$bipartite_rsienaDV 8 2 0
## 240 Network 0 self$bipartite_rsienaDV 8 13 0
## 241 Network 0 self$bipartite_rsienaDV 3 13 0
## 242 Network 0 self$bipartite_rsienaDV 10 6 0
## 243 Network 0 self$bipartite_rsienaDV 1 13 0
## 244 Network 0 self$bipartite_rsienaDV 8 5 0
## 245 Network 0 self$bipartite_rsienaDV 5 8 0
## 246 Network 0 self$bipartite_rsienaDV 11 6 0
## 247 Network 0 self$bipartite_rsienaDV 2 3 0
## 248 Network 0 self$bipartite_rsienaDV 1 3 0
## 249 Network 0 self$bipartite_rsienaDV 4 3 0
## 250 Network 0 self$bipartite_rsienaDV 10 2 0
## 251 Network 0 self$bipartite_rsienaDV 1 5 0
## 252 Network 0 self$bipartite_rsienaDV 11 3 0
## 253 Network 0 self$bipartite_rsienaDV 9 6 0
## 254 Network 0 self$bipartite_rsienaDV 3 5 0
## 255 Network 0 self$bipartite_rsienaDV 5 2 0
## 256 Network 0 self$bipartite_rsienaDV 5 8 0
## 257 Network 0 self$bipartite_rsienaDV 4 3 0
## 258 Network 0 self$bipartite_rsienaDV 6 12 0
## 259 Network 0 self$bipartite_rsienaDV 8 3 0
## 260 Network 0 self$bipartite_rsienaDV 1 5 0
## 261 Network 0 self$bipartite_rsienaDV 8 7 0
## 262 Network 0 self$bipartite_rsienaDV 1 13 0
## 263 Network 0 self$bipartite_rsienaDV 7 11 0
## 264 Network 0 self$bipartite_rsienaDV 4 5 0
## 265 Network 0 self$bipartite_rsienaDV 3 5 0
## 266 Network 0 self$bipartite_rsienaDV 5 7 0
## 267 Network 0 self$bipartite_rsienaDV 6 9 0
## 268 Network 0 self$bipartite_rsienaDV 5 2 0
## 269 Network 0 self$bipartite_rsienaDV 8 13 0
## 270 Network 0 self$bipartite_rsienaDV 12 8 0
## 271 Network 0 self$bipartite_rsienaDV 7 6 0
## 272 Network 0 self$bipartite_rsienaDV 11 2 0
## 273 Network 0 self$bipartite_rsienaDV 5 5 0
## 274 Network 0 self$bipartite_rsienaDV 9 6 0
## 275 Network 0 self$bipartite_rsienaDV 8 4 0
## 276 Network 0 self$bipartite_rsienaDV 11 7 0
## 277 Network 0 self$bipartite_rsienaDV 1 12 0
## 278 Network 0 self$bipartite_rsienaDV 4 3 0
## 279 Network 0 self$bipartite_rsienaDV 12 13 0
## 280 Network 0 self$bipartite_rsienaDV 5 11 0
## 281 Network 0 self$bipartite_rsienaDV 1 10 0
## 282 Network 0 self$bipartite_rsienaDV 10 1 0
## 283 Network 0 self$bipartite_rsienaDV 2 4 0
## 284 Network 0 self$bipartite_rsienaDV 11 7 0
## 285 Network 0 self$bipartite_rsienaDV 8 7 0
## 286 Network 0 self$bipartite_rsienaDV 9 11 0
## 287 Network 0 self$bipartite_rsienaDV 6 1 0
## 288 Network 0 self$bipartite_rsienaDV 9 9 0
## 289 Network 0 self$bipartite_rsienaDV 10 11 0
## 290 Network 0 self$bipartite_rsienaDV 4 7 0
## 291 Network 0 self$bipartite_rsienaDV 8 13 0
## 292 Network 0 self$bipartite_rsienaDV 8 5 0
## 293 Network 0 self$bipartite_rsienaDV 5 7 0
## 294 Network 0 self$bipartite_rsienaDV 6 6 0
## 295 Network 0 self$bipartite_rsienaDV 1 5 0
## 296 Network 0 self$bipartite_rsienaDV 12 13 0
## 297 Network 0 self$bipartite_rsienaDV 1 6 0
## 298 Network 0 self$bipartite_rsienaDV 9 6 0
## 299 Network 0 self$bipartite_rsienaDV 10 6 0
## 300 Network 0 self$bipartite_rsienaDV 7 2 0
## 301 Network 0 self$bipartite_rsienaDV 12 12 0
## 302 Network 0 self$bipartite_rsienaDV 1 6 0
## 303 Network 0 self$bipartite_rsienaDV 1 5 0
## 304 Network 0 self$bipartite_rsienaDV 6 6 0
## 305 Network 0 self$bipartite_rsienaDV 9 12 0
## 306 Network 0 self$bipartite_rsienaDV 12 5 0
## 307 Network 0 self$bipartite_rsienaDV 11 12 0
## 308 Network 0 self$bipartite_rsienaDV 2 4 0
## 309 Network 0 self$bipartite_rsienaDV 7 9 0
## 310 Network 0 self$bipartite_rsienaDV 5 10 0
## 311 Network 0 self$bipartite_rsienaDV 3 12 0
## 312 Network 0 self$bipartite_rsienaDV 5 6 0
## 313 Network 0 self$bipartite_rsienaDV 6 13 0
## 314 Network 0 self$bipartite_rsienaDV 10 2 0
## 315 Network 0 self$bipartite_rsienaDV 7 10 0
## 316 Network 0 self$bipartite_rsienaDV 6 10 0
## 317 Network 0 self$bipartite_rsienaDV 11 9 0
## 318 Network 0 self$bipartite_rsienaDV 6 1 0
## 319 Network 0 self$bipartite_rsienaDV 7 7 0
## 320 Network 0 self$bipartite_rsienaDV 12 2 0
## 321 Network 0 self$bipartite_rsienaDV 4 11 0
## 322 Network 0 self$bipartite_rsienaDV 1 4 0
## 323 Network 0 self$bipartite_rsienaDV 6 11 0
## 324 Network 0 self$bipartite_rsienaDV 1 12 0
## 325 Network 0 self$bipartite_rsienaDV 10 2 0
## 326 Network 0 self$bipartite_rsienaDV 4 12 0
## 327 Network 0 self$bipartite_rsienaDV 12 12 0
## 328 Network 0 self$bipartite_rsienaDV 11 6 0
## 329 Network 0 self$bipartite_rsienaDV 11 3 0
## 330 Network 0 self$bipartite_rsienaDV 4 1 0
## 331 Network 0 self$bipartite_rsienaDV 8 8 0
## 332 Network 0 self$bipartite_rsienaDV 12 4 0
## 333 Network 0 self$bipartite_rsienaDV 2 4 0
## 334 Network 0 self$bipartite_rsienaDV 8 5 0
## 335 Network 0 self$bipartite_rsienaDV 2 5 0
## 336 Network 0 self$bipartite_rsienaDV 8 10 0
## 337 Network 0 self$bipartite_rsienaDV 8 12 0
## 338 Network 0 self$bipartite_rsienaDV 7 1 0
## 339 Network 0 self$bipartite_rsienaDV 9 7 0
## 340 Network 0 self$bipartite_rsienaDV 7 9 0
## 341 Network 0 self$bipartite_rsienaDV 3 3 0
## 342 Network 0 self$bipartite_rsienaDV 12 1 0
## 343 Network 0 self$bipartite_rsienaDV 9 7 0
## 344 Network 0 self$bipartite_rsienaDV 4 1 0
## 345 Network 0 self$bipartite_rsienaDV 11 7 0
## 346 Network 0 self$bipartite_rsienaDV 4 9 0
## 347 Network 0 self$bipartite_rsienaDV 2 8 0
## 348 Network 0 self$bipartite_rsienaDV 7 13 0
## 349 Network 0 self$bipartite_rsienaDV 12 2 0
## 350 Network 0 self$bipartite_rsienaDV 4 4 0
## 351 Network 0 self$bipartite_rsienaDV 9 7 0
## 352 Network 0 self$bipartite_rsienaDV 8 7 0
## 353 Network 0 self$bipartite_rsienaDV 2 5 0
## 354 Network 0 self$bipartite_rsienaDV 3 12 0
## 355 Network 0 self$bipartite_rsienaDV 5 13 0
## 356 Network 0 self$bipartite_rsienaDV 1 13 0
## 357 Network 0 self$bipartite_rsienaDV 6 3 0
## 358 Network 0 self$bipartite_rsienaDV 8 11 0
## 359 Network 0 self$bipartite_rsienaDV 7 3 0
## 360 Network 0 self$bipartite_rsienaDV 4 2 0
## 361 Network 0 self$bipartite_rsienaDV 12 8 0
## 362 Network 0 self$bipartite_rsienaDV 3 9 0
## 363 Network 0 self$bipartite_rsienaDV 1 11 0
## 364 Network 0 self$bipartite_rsienaDV 10 13 0
## 365 Network 0 self$bipartite_rsienaDV 12 13 0
## 366 Network 0 self$bipartite_rsienaDV 7 10 0
## 367 Network 0 self$bipartite_rsienaDV 2 2 0
## 368 Network 0 self$bipartite_rsienaDV 6 7 0
## 369 Network 0 self$bipartite_rsienaDV 5 9 0
## 370 Network 0 self$bipartite_rsienaDV 8 11 0
## 371 Network 0 self$bipartite_rsienaDV 6 5 0
## 372 Network 0 self$bipartite_rsienaDV 9 1 0
## 373 Network 0 self$bipartite_rsienaDV 3 1 0
## 374 Network 0 self$bipartite_rsienaDV 9 4 0
## 375 Network 0 self$bipartite_rsienaDV 12 7 0
## 376 Network 0 self$bipartite_rsienaDV 11 3 0
## 377 Network 0 self$bipartite_rsienaDV 2 13 0
## 378 Network 0 self$bipartite_rsienaDV 10 9 0
## 379 Network 0 self$bipartite_rsienaDV 6 6 0
## 380 Network 0 self$bipartite_rsienaDV 8 6 0
## 381 Network 0 self$bipartite_rsienaDV 2 5 0
## 382 Network 0 self$bipartite_rsienaDV 3 4 0
## 383 Network 0 self$bipartite_rsienaDV 8 5 0
## 384 Network 0 self$bipartite_rsienaDV 1 13 0
## 385 Network 0 self$bipartite_rsienaDV 12 12 0
## 386 Network 0 self$bipartite_rsienaDV 11 2 0
## 387 Network 0 self$bipartite_rsienaDV 6 8 0
## 388 Network 0 self$bipartite_rsienaDV 12 5 0
## 389 Network 0 self$bipartite_rsienaDV 6 7 0
## 390 Network 0 self$bipartite_rsienaDV 2 7 0
## 391 Network 0 self$bipartite_rsienaDV 9 5 0
## 392 Network 0 self$bipartite_rsienaDV 10 2 0
## 393 Network 0 self$bipartite_rsienaDV 12 4 0
## 394 Network 0 self$bipartite_rsienaDV 11 6 0
## 395 Network 0 self$bipartite_rsienaDV 11 10 0
## 396 Network 0 self$bipartite_rsienaDV 12 3 0
## 397 Network 0 self$bipartite_rsienaDV 4 12 0
## 398 Network 0 self$bipartite_rsienaDV 12 1 0
## 399 Network 0 self$bipartite_rsienaDV 12 11 0
## 400 Network 0 self$bipartite_rsienaDV 10 5 0
## 401 Network 0 self$bipartite_rsienaDV 6 13 0
## 402 Network 0 self$bipartite_rsienaDV 6 11 0
## 403 Network 0 self$bipartite_rsienaDV 8 12 0
## 404 Network 0 self$bipartite_rsienaDV 11 1 0
## 405 Network 0 self$bipartite_rsienaDV 6 9 0
## 406 Network 0 self$bipartite_rsienaDV 5 10 0
## 407 Network 0 self$bipartite_rsienaDV 3 4 0
## 408 Network 0 self$bipartite_rsienaDV 4 5 0
## 409 Network 0 self$bipartite_rsienaDV 4 10 0
## 410 Network 0 self$bipartite_rsienaDV 7 13 0
## 411 Network 0 self$bipartite_rsienaDV 4 5 0
## 412 Network 0 self$bipartite_rsienaDV 11 8 0
## 413 Network 0 self$bipartite_rsienaDV 3 7 0
## 414 Network 0 self$bipartite_rsienaDV 12 2 0
## 415 Network 0 self$bipartite_rsienaDV 6 6 0
## 416 Network 0 self$bipartite_rsienaDV 2 7 0
## 417 Network 0 self$bipartite_rsienaDV 8 7 0
## 418 Network 0 self$bipartite_rsienaDV 10 2 0
## 419 Network 0 self$bipartite_rsienaDV 7 12 0
## 420 Network 0 self$bipartite_rsienaDV 9 3 0
## 421 Network 0 self$bipartite_rsienaDV 12 13 0
## 422 Network 0 self$bipartite_rsienaDV 6 5 0
## 423 Network 0 self$bipartite_rsienaDV 9 7 0
## 424 Network 0 self$bipartite_rsienaDV 1 12 0
## 425 Network 0 self$bipartite_rsienaDV 4 11 0
## 426 Network 0 self$bipartite_rsienaDV 8 6 0
## 427 Network 0 self$bipartite_rsienaDV 9 9 0
## 428 Network 0 self$bipartite_rsienaDV 7 4 0
## 429 Network 0 self$bipartite_rsienaDV 9 5 0
## 430 Network 0 self$bipartite_rsienaDV 10 4 0
## 431 Network 0 self$bipartite_rsienaDV 9 12 0
## 432 Network 0 self$bipartite_rsienaDV 10 13 0
## 433 Network 0 self$bipartite_rsienaDV 12 10 0
## 434 Network 0 self$bipartite_rsienaDV 8 13 0
## 435 Network 0 self$bipartite_rsienaDV 6 8 0
## 436 Network 0 self$bipartite_rsienaDV 12 11 0
## 437 Network 0 self$bipartite_rsienaDV 4 6 0
## 438 Network 0 self$bipartite_rsienaDV 9 2 0
## 439 Network 0 self$bipartite_rsienaDV 11 10 0
## 440 Network 0 self$bipartite_rsienaDV 11 10 0
## 441 Network 0 self$bipartite_rsienaDV 6 12 0
## 442 Network 0 self$bipartite_rsienaDV 9 13 0
## 443 Network 0 self$bipartite_rsienaDV 4 5 0
## 444 Network 0 self$bipartite_rsienaDV 10 2 0
## 445 Network 0 self$bipartite_rsienaDV 5 11 0
## 446 Network 0 self$bipartite_rsienaDV 7 5 0
## 447 Network 0 self$bipartite_rsienaDV 2 11 0
## 448 Network 0 self$bipartite_rsienaDV 7 9 0
## 449 Network 0 self$bipartite_rsienaDV 10 4 0
## 450 Network 0 self$bipartite_rsienaDV 12 9 0
## 451 Network 0 self$bipartite_rsienaDV 3 3 0
## 452 Network 0 self$bipartite_rsienaDV 11 4 0
## 453 Network 0 self$bipartite_rsienaDV 5 8 0
## 454 Network 0 self$bipartite_rsienaDV 10 4 0
## 455 Network 0 self$bipartite_rsienaDV 6 10 0
## 456 Network 0 self$bipartite_rsienaDV 10 10 0
## 457 Network 0 self$bipartite_rsienaDV 1 13 0
## 458 Network 0 self$bipartite_rsienaDV 7 1 0
## 459 Network 0 self$bipartite_rsienaDV 9 5 0
## 460 Network 0 self$bipartite_rsienaDV 1 2 0
## 461 Network 0 self$bipartite_rsienaDV 10 10 0
## 462 Network 0 self$bipartite_rsienaDV 12 9 0
## 463 Network 0 self$bipartite_rsienaDV 1 7 0
## 464 Network 0 self$bipartite_rsienaDV 10 4 0
## 465 Network 0 self$bipartite_rsienaDV 8 8 0
## 466 Network 0 self$bipartite_rsienaDV 8 5 0
## 467 Network 0 self$bipartite_rsienaDV 5 8 0
## 468 Network 0 self$bipartite_rsienaDV 3 6 0
## 469 Network 0 self$bipartite_rsienaDV 12 7 0
## 470 Network 0 self$bipartite_rsienaDV 9 1 0
## 471 Network 0 self$bipartite_rsienaDV 2 1 0
## 472 Network 0 self$bipartite_rsienaDV 2 13 0
## 473 Network 0 self$bipartite_rsienaDV 1 2 0
## 474 Network 0 self$bipartite_rsienaDV 12 2 0
## 475 Network 0 self$bipartite_rsienaDV 10 7 0
## 476 Network 0 self$bipartite_rsienaDV 3 4 0
## 477 Network 0 self$bipartite_rsienaDV 8 9 0
## 478 Network 0 self$bipartite_rsienaDV 3 13 0
## 479 Network 0 self$bipartite_rsienaDV 10 2 0
## 480 Network 0 self$bipartite_rsienaDV 8 13 0
## 481 Network 0 self$bipartite_rsienaDV 1 6 0
## 482 Network 0 self$bipartite_rsienaDV 9 4 0
## 483 Network 0 self$bipartite_rsienaDV 11 8 0
## 484 Network 0 self$bipartite_rsienaDV 10 2 0
## 485 Network 0 self$bipartite_rsienaDV 1 4 0
## 486 Network 0 self$bipartite_rsienaDV 10 8 0
## 487 Network 0 self$bipartite_rsienaDV 9 10 0
## 488 Network 0 self$bipartite_rsienaDV 1 11 0
## 489 Network 0 self$bipartite_rsienaDV 6 9 0
## 490 Network 0 self$bipartite_rsienaDV 7 1 0
## 491 Network 0 self$bipartite_rsienaDV 2 5 0
## 492 Network 0 self$bipartite_rsienaDV 2 13 0
## 493 Network 0 self$bipartite_rsienaDV 5 7 0
## 494 Network 0 self$bipartite_rsienaDV 4 6 0
## 495 Network 0 self$bipartite_rsienaDV 1 8 0
## 496 Network 0 self$bipartite_rsienaDV 10 9 0
## 497 Network 0 self$bipartite_rsienaDV 7 12 0
## 498 Network 0 self$bipartite_rsienaDV 5 6 0
## 499 Network 0 self$bipartite_rsienaDV 10 7 0
## 500 Network 0 self$bipartite_rsienaDV 11 4 0
## 501 Network 0 self$bipartite_rsienaDV 5 10 0
## 502 Network 0 self$bipartite_rsienaDV 10 7 0
## 503 Network 0 self$bipartite_rsienaDV 7 6 0
## 504 Network 0 self$bipartite_rsienaDV 10 2 0
## 505 Network 0 self$bipartite_rsienaDV 1 13 0
## 506 Network 0 self$bipartite_rsienaDV 4 7 0
## 507 Network 0 self$bipartite_rsienaDV 11 13 0
## 508 Network 0 self$bipartite_rsienaDV 11 9 0
## 509 Network 0 self$bipartite_rsienaDV 2 10 0
## 510 Network 0 self$bipartite_rsienaDV 7 1 0
## 511 Network 0 self$bipartite_rsienaDV 5 9 0
## 512 Network 0 self$bipartite_rsienaDV 11 6 0
## 513 Network 0 self$bipartite_rsienaDV 1 7 0
## 514 Network 0 self$bipartite_rsienaDV 8 6 0
## 515 Network 0 self$bipartite_rsienaDV 3 7 0
## 516 Network 0 self$bipartite_rsienaDV 10 2 0
## 517 Network 0 self$bipartite_rsienaDV 1 5 0
## 518 Network 0 self$bipartite_rsienaDV 11 4 0
## 519 Network 0 self$bipartite_rsienaDV 2 11 0
## 520 Network 0 self$bipartite_rsienaDV 6 1 0
## 521 Network 0 self$bipartite_rsienaDV 11 6 0
## 522 Network 0 self$bipartite_rsienaDV 9 6 0
## 523 Network 0 self$bipartite_rsienaDV 8 4 0
## 524 Network 0 self$bipartite_rsienaDV 7 6 0
## 525 Network 0 self$bipartite_rsienaDV 4 7 0
## 526 Network 0 self$bipartite_rsienaDV 4 9 0
## 527 Network 0 self$bipartite_rsienaDV 3 12 0
## 528 Network 0 self$bipartite_rsienaDV 5 13 0
## 529 Network 0 self$bipartite_rsienaDV 12 12 0
## 530 Network 0 self$bipartite_rsienaDV 3 3 0
## 531 Network 0 self$bipartite_rsienaDV 3 6 0
## 532 Network 0 self$bipartite_rsienaDV 8 5 0
## 533 Network 0 self$bipartite_rsienaDV 6 5 0
## 534 Network 0 self$bipartite_rsienaDV 11 12 0
## 535 Network 0 self$bipartite_rsienaDV 10 7 0
## 536 Network 0 self$bipartite_rsienaDV 7 13 0
## 537 Network 0 self$bipartite_rsienaDV 11 11 0
## 538 Network 0 self$bipartite_rsienaDV 3 3 0
## 539 Network 0 self$bipartite_rsienaDV 12 6 0
## 540 Network 0 self$bipartite_rsienaDV 1 10 0
## 541 Network 0 self$bipartite_rsienaDV 11 6 0
## 542 Network 0 self$bipartite_rsienaDV 2 2 0
## 543 Network 0 self$bipartite_rsienaDV 5 6 0
## 544 Network 0 self$bipartite_rsienaDV 3 13 0
## 545 Network 0 self$bipartite_rsienaDV 11 13 0
## 546 Network 0 self$bipartite_rsienaDV 1 8 0
## 547 Network 0 self$bipartite_rsienaDV 6 9 0
## 548 Network 0 self$bipartite_rsienaDV 12 9 0
## 549 Network 0 self$bipartite_rsienaDV 11 3 0
## 550 Network 0 self$bipartite_rsienaDV 5 13 0
## 551 Network 0 self$bipartite_rsienaDV 2 3 0
## 552 Network 0 self$bipartite_rsienaDV 9 9 0
## 553 Network 0 self$bipartite_rsienaDV 12 6 0
## 554 Network 0 self$bipartite_rsienaDV 4 13 0
## 555 Network 0 self$bipartite_rsienaDV 9 2 0
## 556 Network 0 self$bipartite_rsienaDV 11 10 0
## 557 Network 0 self$bipartite_rsienaDV 1 9 0
## 558 Network 0 self$bipartite_rsienaDV 2 10 0
## 559 Network 0 self$bipartite_rsienaDV 1 13 0
## 560 Network 0 self$bipartite_rsienaDV 1 13 0
## 561 Network 0 self$bipartite_rsienaDV 2 5 0
## 562 Network 0 self$bipartite_rsienaDV 9 7 0
## 563 Network 0 self$bipartite_rsienaDV 2 9 0
## 564 Network 0 self$bipartite_rsienaDV 6 11 0
## 565 Network 0 self$bipartite_rsienaDV 6 1 0
## 566 Network 0 self$bipartite_rsienaDV 4 7 0
## 567 Network 0 self$bipartite_rsienaDV 10 3 0
## 568 Network 0 self$bipartite_rsienaDV 8 6 0
## 569 Network 0 self$bipartite_rsienaDV 3 9 0
## 570 Network 0 self$bipartite_rsienaDV 3 4 0
## 571 Network 0 self$bipartite_rsienaDV 4 2 0
## 572 Network 0 self$bipartite_rsienaDV 8 2 0
## 573 Network 0 self$bipartite_rsienaDV 4 7 0
## 574 Network 0 self$bipartite_rsienaDV 1 2 0
## 575 Network 0 self$bipartite_rsienaDV 1 10 0
## 576 Network 0 self$bipartite_rsienaDV 3 4 0
## 577 Network 0 self$bipartite_rsienaDV 4 10 0
## 578 Network 0 self$bipartite_rsienaDV 7 9 0
## 579 Network 0 self$bipartite_rsienaDV 5 9 0
## 580 Network 0 self$bipartite_rsienaDV 4 5 0
## 581 Network 0 self$bipartite_rsienaDV 12 6 0
## 582 Network 0 self$bipartite_rsienaDV 1 1 0
## 583 Network 0 self$bipartite_rsienaDV 5 5 0
## 584 Network 0 self$bipartite_rsienaDV 10 12 0
## 585 Network 0 self$bipartite_rsienaDV 5 2 0
## 586 Network 0 self$bipartite_rsienaDV 6 3 0
## 587 Network 0 self$bipartite_rsienaDV 2 1 0
## 588 Network 0 self$bipartite_rsienaDV 2 3 0
## 589 Network 0 self$bipartite_rsienaDV 2 11 0
## 590 Network 0 self$bipartite_rsienaDV 11 10 0
## 591 Network 0 self$bipartite_rsienaDV 6 10 0
## 592 Network 0 self$bipartite_rsienaDV 10 10 0
## 593 Network 0 self$bipartite_rsienaDV 5 2 0
## 594 Network 0 self$bipartite_rsienaDV 3 10 0
## 595 Network 0 self$bipartite_rsienaDV 3 10 0
## 596 Network 0 self$bipartite_rsienaDV 2 5 0
## 597 Network 0 self$bipartite_rsienaDV 6 11 0
## 598 Network 0 self$bipartite_rsienaDV 1 13 0
## 599 Network 0 self$bipartite_rsienaDV 4 10 0
## 600 Network 0 self$bipartite_rsienaDV 3 3 0
## reciprocal_rate LogOptionSetProb LogChoiceProb diagonal stability
## 1 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 2 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 3 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 4 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 5 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 6 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 7 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 8 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 9 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 10 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 11 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 12 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 13 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 14 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 15 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 16 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 17 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 18 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 19 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 20 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 21 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 22 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 23 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 24 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 25 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 26 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 27 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 28 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 29 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 30 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 31 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 32 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 33 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 34 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 35 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 36 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 37 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 38 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 39 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 40 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 41 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 42 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 43 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 44 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 45 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 46 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 47 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 48 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 49 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 50 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 51 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 52 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 53 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 54 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 55 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 56 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 57 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 58 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 59 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 60 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 61 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 62 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 63 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 64 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 65 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 66 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 67 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 68 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 69 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 70 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 71 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 72 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 73 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 74 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 75 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 76 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 77 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 78 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 79 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 80 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 81 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 82 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 83 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 84 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 85 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 86 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 87 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 88 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 89 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 90 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 91 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 92 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 93 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 94 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 95 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 96 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 97 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 98 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 99 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 100 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 101 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 102 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 103 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 104 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 105 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 106 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 107 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 108 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 109 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 110 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 111 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 112 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 113 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 114 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 115 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 116 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 117 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 118 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 119 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 120 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 121 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 122 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 123 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 124 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 125 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 126 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 127 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 128 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 129 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 130 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 131 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 132 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 133 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 134 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 135 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 136 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 137 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 138 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 139 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 140 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 141 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 142 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 143 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 144 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 145 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 146 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 147 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 148 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 149 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 150 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 151 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 152 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 153 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 154 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 155 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 156 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 157 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 158 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 159 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 160 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 161 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 162 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 163 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 164 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 165 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 166 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 167 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 168 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 169 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 170 0.08333333 -2.484907 -2.602084 FALSE FALSE
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## 541 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 542 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 543 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 544 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 545 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 546 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 547 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 548 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 549 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 550 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 551 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 552 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 553 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 554 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 555 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 556 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 557 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 558 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 559 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 560 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 561 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 562 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 563 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 564 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 565 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 566 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 567 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 568 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 569 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 570 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 571 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 572 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 573 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 574 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 575 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 576 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 577 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 578 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 579 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 580 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 581 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 582 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 583 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 584 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 585 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 586 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 587 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 588 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 589 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 590 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 591 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 592 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 593 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 594 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 595 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 596 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 597 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 598 0.08333333 -2.484907 -2.202084 FALSE TRUE
## 599 0.08333333 -2.484907 -2.602084 FALSE FALSE
## 600 0.08333333 -2.484907 -2.602084 FALSE FALSE
## tie_change chain_step_id
## 1 TRUE 1
## 2 TRUE 2
## 3 TRUE 3
## 4 TRUE 4
## 5 TRUE 5
## 6 FALSE 5
## 7 TRUE 7
## 8 TRUE 8
## 9 TRUE 9
## 10 TRUE 10
## 11 TRUE 11
## 12 TRUE 12
## 13 FALSE 12
## 14 TRUE 14
## 15 TRUE 15
## 16 TRUE 16
## 17 FALSE 16
## 18 TRUE 18
## 19 TRUE 19
## 20 TRUE 20
## 21 FALSE 20
## 22 TRUE 22
## 23 TRUE 23
## 24 TRUE 24
## 25 FALSE 24
## 26 TRUE 26
## 27 TRUE 27
## 28 TRUE 28
## 29 FALSE 28
## 30 FALSE 28
## 31 TRUE 31
## 32 TRUE 32
## 33 FALSE 32
## 34 TRUE 34
## 35 TRUE 35
## 36 TRUE 36
## 37 TRUE 37
## 38 TRUE 38
## 39 TRUE 39
## 40 TRUE 40
## 41 FALSE 40
## 42 TRUE 42
## 43 TRUE 43
## 44 TRUE 44
## 45 TRUE 45
## 46 FALSE 45
## 47 TRUE 47
## 48 TRUE 48
## 49 TRUE 49
## 50 TRUE 50
## 51 FALSE 50
## 52 FALSE 50
## 53 TRUE 53
## 54 TRUE 54
## 55 TRUE 55
## 56 TRUE 56
## 57 TRUE 57
## 58 TRUE 58
## 59 TRUE 59
## 60 TRUE 60
## 61 TRUE 61
## 62 TRUE 62
## 63 FALSE 62
## 64 TRUE 64
## 65 TRUE 65
## 66 TRUE 66
## 67 FALSE 66
## 68 FALSE 66
## 69 TRUE 69
## 70 TRUE 70
## 71 TRUE 71
## 72 FALSE 71
## 73 TRUE 73
## 74 TRUE 74
## 75 TRUE 75
## 76 TRUE 76
## 77 TRUE 77
## 78 TRUE 78
## 79 TRUE 79
## 80 TRUE 80
## 81 TRUE 81
## 82 TRUE 82
## 83 TRUE 83
## 84 TRUE 84
## 85 TRUE 85
## 86 TRUE 86
## 87 TRUE 87
## 88 TRUE 88
## 89 FALSE 88
## 90 TRUE 90
## 91 TRUE 91
## 92 TRUE 92
## 93 TRUE 93
## 94 TRUE 94
## 95 TRUE 95
## 96 TRUE 96
## 97 TRUE 97
## 98 TRUE 98
## 99 TRUE 99
## 100 TRUE 100
## 101 FALSE 100
## 102 TRUE 102
## 103 TRUE 103
## 104 TRUE 104
## 105 TRUE 105
## 106 TRUE 106
## 107 TRUE 107
## 108 TRUE 108
## 109 TRUE 109
## 110 TRUE 110
## 111 TRUE 111
## 112 TRUE 112
## 113 TRUE 113
## 114 TRUE 114
## 115 TRUE 115
## 116 TRUE 116
## 117 TRUE 117
## 118 TRUE 118
## 119 TRUE 119
## 120 TRUE 120
## 121 TRUE 121
## 122 TRUE 122
## 123 TRUE 123
## 124 FALSE 123
## 125 TRUE 125
## 126 TRUE 126
## 127 TRUE 127
## 128 TRUE 128
## 129 TRUE 129
## 130 TRUE 130
## 131 TRUE 131
## 132 TRUE 132
## 133 TRUE 133
## 134 TRUE 134
## 135 TRUE 135
## 136 TRUE 136
## 137 TRUE 137
## 138 FALSE 137
## 139 TRUE 139
## 140 TRUE 140
## 141 TRUE 141
## 142 TRUE 142
## 143 TRUE 143
## 144 TRUE 144
## 145 FALSE 144
## 146 TRUE 146
## 147 TRUE 147
## 148 FALSE 147
## 149 TRUE 149
## 150 TRUE 150
## 151 TRUE 151
## 152 TRUE 152
## 153 TRUE 153
## 154 TRUE 154
## 155 TRUE 155
## 156 TRUE 156
## 157 FALSE 156
## 158 TRUE 158
## 159 TRUE 159
## 160 TRUE 160
## 161 TRUE 161
## 162 TRUE 162
## 163 TRUE 163
## 164 TRUE 164
## 165 TRUE 165
## 166 FALSE 165
## 167 TRUE 167
## 168 TRUE 168
## 169 TRUE 169
## 170 TRUE 170
## 171 TRUE 171
## 172 TRUE 172
## 173 TRUE 173
## 174 TRUE 174
## 175 TRUE 175
## 176 FALSE 175
## 177 TRUE 177
## 178 TRUE 178
## 179 FALSE 178
## 180 TRUE 180
## 181 FALSE 180
## 182 FALSE 180
## 183 FALSE 180
## 184 TRUE 184
## 185 TRUE 185
## 186 TRUE 186
## 187 TRUE 187
## 188 TRUE 188
## 189 TRUE 189
## 190 TRUE 190
## 191 TRUE 191
## 192 TRUE 192
## 193 TRUE 193
## 194 TRUE 194
## 195 TRUE 195
## 196 TRUE 196
## 197 TRUE 197
## 198 TRUE 198
## 199 TRUE 199
## 200 TRUE 200
## 201 TRUE 201
## 202 TRUE 202
## 203 TRUE 203
## 204 TRUE 204
## 205 TRUE 205
## 206 FALSE 205
## 207 TRUE 207
## 208 FALSE 207
## 209 TRUE 209
## 210 TRUE 210
## 211 TRUE 211
## 212 TRUE 212
## 213 TRUE 213
## 214 FALSE 213
## 215 TRUE 215
## 216 TRUE 216
## 217 TRUE 217
## 218 TRUE 218
## 219 TRUE 219
## 220 TRUE 220
## 221 TRUE 221
## 222 TRUE 222
## 223 TRUE 223
## 224 TRUE 224
## 225 TRUE 225
## 226 TRUE 226
## 227 TRUE 227
## 228 TRUE 228
## 229 TRUE 229
## 230 TRUE 230
## 231 TRUE 231
## 232 TRUE 232
## 233 TRUE 233
## 234 TRUE 234
## 235 FALSE 234
## 236 TRUE 236
## 237 TRUE 237
## 238 TRUE 238
## 239 TRUE 239
## 240 FALSE 239
## 241 FALSE 239
## 242 TRUE 242
## 243 FALSE 242
## 244 TRUE 244
## 245 TRUE 245
## 246 TRUE 246
## 247 TRUE 247
## 248 TRUE 248
## 249 TRUE 249
## 250 TRUE 250
## 251 TRUE 251
## 252 TRUE 252
## 253 TRUE 253
## 254 TRUE 254
## 255 TRUE 255
## 256 TRUE 256
## 257 TRUE 257
## 258 TRUE 258
## 259 TRUE 259
## 260 TRUE 260
## 261 TRUE 261
## 262 FALSE 261
## 263 TRUE 263
## 264 TRUE 264
## 265 TRUE 265
## 266 TRUE 266
## 267 TRUE 267
## 268 TRUE 268
## 269 FALSE 268
## 270 TRUE 270
## 271 TRUE 271
## 272 TRUE 272
## 273 TRUE 273
## 274 TRUE 274
## 275 TRUE 275
## 276 TRUE 276
## 277 TRUE 277
## 278 TRUE 278
## 279 FALSE 278
## 280 TRUE 280
## 281 TRUE 281
## 282 TRUE 282
## 283 TRUE 283
## 284 TRUE 284
## 285 TRUE 285
## 286 TRUE 286
## 287 TRUE 287
## 288 TRUE 288
## 289 TRUE 289
## 290 TRUE 290
## 291 FALSE 290
## 292 TRUE 292
## 293 TRUE 293
## 294 TRUE 294
## 295 TRUE 295
## 296 FALSE 295
## 297 TRUE 297
## 298 TRUE 298
## 299 TRUE 299
## 300 TRUE 300
## 301 TRUE 301
## 302 TRUE 302
## 303 TRUE 303
## 304 TRUE 304
## 305 TRUE 305
## 306 TRUE 306
## 307 TRUE 307
## 308 TRUE 308
## 309 TRUE 309
## 310 TRUE 310
## 311 TRUE 311
## 312 TRUE 312
## 313 FALSE 312
## 314 TRUE 314
## 315 TRUE 315
## 316 TRUE 316
## 317 TRUE 317
## 318 TRUE 318
## 319 TRUE 319
## 320 TRUE 320
## 321 TRUE 321
## 322 TRUE 322
## 323 TRUE 323
## 324 TRUE 324
## 325 TRUE 325
## 326 TRUE 326
## 327 TRUE 327
## 328 TRUE 328
## 329 TRUE 329
## 330 TRUE 330
## 331 TRUE 331
## 332 TRUE 332
## 333 TRUE 333
## 334 TRUE 334
## 335 TRUE 335
## 336 TRUE 336
## 337 TRUE 337
## 338 TRUE 338
## 339 TRUE 339
## 340 TRUE 340
## 341 TRUE 341
## 342 TRUE 342
## 343 TRUE 343
## 344 TRUE 344
## 345 TRUE 345
## 346 TRUE 346
## 347 TRUE 347
## 348 FALSE 347
## 349 TRUE 349
## 350 TRUE 350
## 351 TRUE 351
## 352 TRUE 352
## 353 TRUE 353
## 354 TRUE 354
## 355 FALSE 354
## 356 FALSE 354
## 357 TRUE 357
## 358 TRUE 358
## 359 TRUE 359
## 360 TRUE 360
## 361 TRUE 361
## 362 TRUE 362
## 363 TRUE 363
## 364 FALSE 363
## 365 FALSE 363
## 366 TRUE 366
## 367 TRUE 367
## 368 TRUE 368
## 369 TRUE 369
## 370 TRUE 370
## 371 TRUE 371
## 372 TRUE 372
## 373 TRUE 373
## 374 TRUE 374
## 375 TRUE 375
## 376 TRUE 376
## 377 FALSE 376
## 378 TRUE 378
## 379 TRUE 379
## 380 TRUE 380
## 381 TRUE 381
## 382 TRUE 382
## 383 TRUE 383
## 384 FALSE 383
## 385 TRUE 385
## 386 TRUE 386
## 387 TRUE 387
## 388 TRUE 388
## 389 TRUE 389
## 390 TRUE 390
## 391 TRUE 391
## 392 TRUE 392
## 393 TRUE 393
## 394 TRUE 394
## 395 TRUE 395
## 396 TRUE 396
## 397 TRUE 397
## 398 TRUE 398
## 399 TRUE 399
## 400 TRUE 400
## 401 FALSE 400
## 402 TRUE 402
## 403 TRUE 403
## 404 TRUE 404
## 405 TRUE 405
## 406 TRUE 406
## 407 TRUE 407
## 408 TRUE 408
## 409 TRUE 409
## 410 FALSE 409
## 411 TRUE 411
## 412 TRUE 412
## 413 TRUE 413
## 414 TRUE 414
## 415 TRUE 415
## 416 TRUE 416
## 417 TRUE 417
## 418 TRUE 418
## 419 TRUE 419
## 420 TRUE 420
## 421 FALSE 420
## 422 TRUE 422
## 423 TRUE 423
## 424 TRUE 424
## 425 TRUE 425
## 426 TRUE 426
## 427 TRUE 427
## 428 TRUE 428
## 429 TRUE 429
## 430 TRUE 430
## 431 TRUE 431
## 432 FALSE 431
## 433 TRUE 433
## 434 FALSE 433
## 435 TRUE 435
## 436 TRUE 436
## 437 TRUE 437
## 438 TRUE 438
## 439 TRUE 439
## 440 TRUE 440
## 441 TRUE 441
## 442 FALSE 441
## 443 TRUE 443
## 444 TRUE 444
## 445 TRUE 445
## 446 TRUE 446
## 447 TRUE 447
## 448 TRUE 448
## 449 TRUE 449
## 450 TRUE 450
## 451 TRUE 451
## 452 TRUE 452
## 453 TRUE 453
## 454 TRUE 454
## 455 TRUE 455
## 456 TRUE 456
## 457 FALSE 456
## 458 TRUE 458
## 459 TRUE 459
## 460 TRUE 460
## 461 TRUE 461
## 462 TRUE 462
## 463 TRUE 463
## 464 TRUE 464
## 465 TRUE 465
## 466 TRUE 466
## 467 TRUE 467
## 468 TRUE 468
## 469 TRUE 469
## 470 TRUE 470
## 471 TRUE 471
## 472 FALSE 471
## 473 TRUE 473
## 474 TRUE 474
## 475 TRUE 475
## 476 TRUE 476
## 477 TRUE 477
## 478 FALSE 477
## 479 TRUE 479
## 480 FALSE 479
## 481 TRUE 481
## 482 TRUE 482
## 483 TRUE 483
## 484 TRUE 484
## 485 TRUE 485
## 486 TRUE 486
## 487 TRUE 487
## 488 TRUE 488
## 489 TRUE 489
## 490 TRUE 490
## 491 TRUE 491
## 492 FALSE 491
## 493 TRUE 493
## 494 TRUE 494
## 495 TRUE 495
## 496 TRUE 496
## 497 TRUE 497
## 498 TRUE 498
## 499 TRUE 499
## 500 TRUE 500
## 501 TRUE 501
## 502 TRUE 502
## 503 TRUE 503
## 504 TRUE 504
## 505 FALSE 504
## 506 TRUE 506
## 507 FALSE 506
## 508 TRUE 508
## 509 TRUE 509
## 510 TRUE 510
## 511 TRUE 511
## 512 TRUE 512
## 513 TRUE 513
## 514 TRUE 514
## 515 TRUE 515
## 516 TRUE 516
## 517 TRUE 517
## 518 TRUE 518
## 519 TRUE 519
## 520 TRUE 520
## 521 TRUE 521
## 522 TRUE 522
## 523 TRUE 523
## 524 TRUE 524
## 525 TRUE 525
## 526 TRUE 526
## 527 TRUE 527
## 528 FALSE 527
## 529 TRUE 529
## 530 TRUE 530
## 531 TRUE 531
## 532 TRUE 532
## 533 TRUE 533
## 534 TRUE 534
## 535 TRUE 535
## 536 FALSE 535
## 537 TRUE 537
## 538 TRUE 538
## 539 TRUE 539
## 540 TRUE 540
## 541 TRUE 541
## 542 TRUE 542
## 543 TRUE 543
## 544 FALSE 543
## 545 FALSE 543
## 546 TRUE 546
## 547 TRUE 547
## 548 TRUE 548
## 549 TRUE 549
## 550 FALSE 549
## 551 TRUE 551
## 552 TRUE 552
## 553 TRUE 553
## 554 FALSE 553
## 555 TRUE 555
## 556 TRUE 556
## 557 TRUE 557
## 558 TRUE 558
## 559 FALSE 558
## 560 FALSE 558
## 561 TRUE 561
## 562 TRUE 562
## 563 TRUE 563
## 564 TRUE 564
## 565 TRUE 565
## 566 TRUE 566
## 567 TRUE 567
## 568 TRUE 568
## 569 TRUE 569
## 570 TRUE 570
## 571 TRUE 571
## 572 TRUE 572
## 573 TRUE 573
## 574 TRUE 574
## 575 TRUE 575
## 576 TRUE 576
## 577 TRUE 577
## 578 TRUE 578
## 579 TRUE 579
## 580 TRUE 580
## 581 TRUE 581
## 582 TRUE 582
## 583 TRUE 583
## 584 TRUE 584
## 585 TRUE 585
## 586 TRUE 586
## 587 TRUE 587
## 588 TRUE 588
## 589 TRUE 589
## 590 TRUE 590
## 591 TRUE 591
## 592 TRUE 592
## 593 TRUE 593
## 594 TRUE 594
## 595 TRUE 595
## 596 TRUE 596
## 597 TRUE 597
## 598 FALSE 597
## 599 TRUE 599
## 600 TRUE 600
##
## 16.67%
## 33.33%
## 50.00%
## 66.67%
## 83.33%
## 100.00%
## ===================================================================================================
## Model 1
## ---------------------------------------------------------------------------------------------------
## Rate parameter period 1 0.0788 (0.0747)
## outdegree (density) 0.0000 (0.0000)
## indegree - popularity 0.0000 (0.0000)
## outdegree - activity 0.0000 (0.0000)
## XW=>X closure of self$component_1_coDyadCovar 0.1000 (0.0000) ***
## self$component_2_coDyadCovar 0.0000 (0.0000)
## self$strat_1_coCovar ego 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar 0.0000 (0.0000)
## self$strat_3_coCovar tot in-alter dist 2 0.0000 (0.0000)
## self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.0000 (0.0000)
## self$strat_3_coCovar tot in-alter dist 2 x self$component_2_coDyadCovar 0.0000 (0.0000)
## ---------------------------------------------------------------------------------------------------
## Iterations 600
## ===================================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05## 1st and last state of the bipartite matrix system
print(env1$bipartite_matrix_init )## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 1 1 1 1 1 1 1 1 1 1 1 1
## [3,] 1 1 1 1 1 1 1 1 1 1 1 1
## [4,] 1 1 1 1 1 1 1 1 1 1 1 1
## [5,] 1 1 1 1 1 1 1 1 1 1 1 1
## [6,] 1 1 1 1 1 1 1 1 1 1 1 1
## [7,] 1 1 1 1 1 1 1 1 1 1 1 1
## [8,] 1 1 1 1 1 1 1 1 1 1 1 1
## [9,] 1 1 1 1 1 1 1 1 1 1 1 1
## [10,] 1 1 1 1 1 1 1 1 1 1 1 1
## [11,] 1 1 1 1 1 1 1 1 1 1 1 1
## [12,] 1 1 1 1 1 1 1 1 1 1 1 1print(env1$bi_env_arr[,, dim(env1$bi_env_arr)[3] ] )## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1 0 0 1 1 1 0 0 1 0 0 1
## [2,] 0 0 0 0 1 1 0 0 0 1 1 1
## [3,] 1 0 0 0 1 1 1 0 0 0 0 0
## [4,] 1 1 0 0 1 1 1 0 1 0 1 1
## [5,] 1 1 1 0 0 1 1 1 0 1 1 1
## [6,] 0 1 1 1 0 0 0 0 0 0 1 0
## [7,] 0 0 1 1 1 0 1 1 1 1 0 1
## [8,] 0 1 1 0 0 0 0 0 0 1 1 0
## [9,] 0 0 0 0 0 0 0 0 0 1 0 1
## [10,] 1 0 0 1 0 0 1 0 0 0 1 0
## [11,] 1 1 0 1 1 0 1 0 1 0 0 0
## [12,] 1 0 0 1 1 1 1 0 1 0 0 11.4 Print Bipartite Search Network Snapshots
Snapshopts of the the biparite network and the social and component epistasis interactions are taken at fixed step intervals to show the evolving multidimensional coupled search environment (actors and components)
# snapshot_ids <- round( seq(1, dim(env1$bi_env_arr)[3], length=3 ) )
# snapshot_ids <- c(1, 1+ (1:6)*env1$M)
snapshot_ids <- c(1, (1:8)*10, dim(env1$bi_env_arr)[3] )
for (i in 1:length(snapshot_ids)) {
step <- snapshot_ids[ i ]
mat <- env1$bi_env_arr[,,step]
env1$plot_bipartite_system_from_mat(mat, step)
}
1.5 Plot Actor Degrees (Component Scope and Common Affiliation Social Ties)
Time series is simulated decision steps.
##
env1$plot_actor_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.6 Plot Component Degrees (Membership/Popularity and Common Actor Epistasis Ties)
##
env1$plot_component_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.7 Plot Utility by Strategy over Time (Simulated Decision Steps)
## [1] 7200 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.8 Plot Utility by Actor over Time (Simulated Decision Steps)
1.9 Plot Contributions to Actor Utility colored by Strategy
Time series is simulated decision steps.
##
env1$plot_utility_components(loess_span=0.25)## `geom_smooth()` using formula = 'y ~ x'